System Design: Two Production-Ready Services
This interview requires designing two distinct, production-ready services: a Thread-Safe LRU Caching Service and a Compute-Heavy "Find Transfer Combinations" API.
A) Thread-Safe LRU Caching Service
Design and describe a production-ready, in-process LRU cache that can also be deployed as a networked cache service.
Specify:
- API surface (in-process library and optional network endpoints):
get,put,delete,computeIfAbsent, per-entry TTL, size limits, stats. - Concurrency control inside a process (e.g., fine-grained locks, lock striping, or lock-free techniques). Ensure eviction is correct under concurrent access.
- TTL and capacity management (entry count limit, weighted by bytes, admission control).
- Metrics and observability (hits/misses, evictions, latencies, saturation, traces, logs, debug endpoints).
- Scaling across multiple instances:
- Using Redis (or another remote cache): topology, client behavior, backpressure, failure handling.
- Or client-side sharding with consistent hashing: hot key mitigation, replication/failover, rebalancing, consistency model.
- Handling hot keys, backpressure, and data consistency semantics (cache-aside/write-through, stale reads, idempotency of updates, Compare-And-Swap (CAS)).
Provide concrete choices and trade-offs.
B) Compute-Heavy "Find Transfer Combinations" API
Design an API that computes transfer route combinations (e.g., finding valid paths across payment rails/providers with constraints), with potential traffic spikes.
Specify:
- Stateless vs. stateful design; avoid shared mutable state in workers and define per-request memory management.
- Caching and precomputation strategies; request de-duplication (in-process and cross-instance).
- Rate limiting, timeouts/deadlines, cancellation, idempotency.
- Horizontal scaling and autoscaling policies; synchronous vs. async job modes.
- Capacity estimates with clear assumptions and formulas.
- A testing plan covering correctness, performance, reliability, and operability.
Make any minimal, explicit assumptions needed so the design is concrete.
AI interview prep
Core dilemma
Designing two distinct, production-ready services—one for high-throughput, low-latency caching, and another for bursty, compute-intensive operations—while managing their unique challenges and potential interactions. This involves balancing strict correctness and consistency requirements with extreme performance, scalability, and resource efficiency under heavy load and failure conditions.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
System Design: Two Production-Ready Services
This interview requires designing two distinct, production-ready services: a Thread-Safe LRU Caching Service and a Compute-Heavy "Find Transfer Combinations" API.
A) Thread-Safe LRU Caching Service
Design and describe a production-ready, in-process LRU cache that can also be deployed as a networked cache service.
Specify:
- API surface (in-process library and optional network endpoints):
get,put,delete,computeIfAbsent, per-entry TTL, size limits, stats. - Concurrency control inside a process (e.g., fine-grained locks, lock striping, or lock-free techniques). Ensure eviction is correct under concurrent access.
- TTL and capacity management (entry count limit, weighted by bytes, admission control).
- Metrics and observability (hits/misses, evictions, latencies, saturation, traces, logs, debug endpoints).
- Scaling across multiple instances:
- Using Redis (or another remote cache): topology, client behavior, backpressure, failure handling.
- Or client-side sharding with consistent hashing: hot key mitigation, replication/failover, rebalancing, consistency model.
- Handling hot keys, backpressure, and data consistency semantics (cache-aside/write-through, stale reads, idempotency of updates, Compare-And-Swap (CAS)).
Provide concrete choices and trade-offs.
B) Compute-Heavy "Find Transfer Combinations" API
Design an API that computes transfer route combinations (e.g., finding valid paths across payment rails/providers with constraints), with potential traffic spikes.
Specify:
- Stateless vs. stateful design; avoid shared mutable state in workers and define per-request memory management.
- Caching and precomputation strategies; request de-duplication (in-process and cross-instance).
- Rate limiting, timeouts/deadlines, cancellation, idempotency.
- Horizontal scaling and autoscaling policies; synchronous vs. async job modes.
- Capacity estimates with clear assumptions and formulas.
- A testing plan covering correctness, performance, reliability, and operability.
Make any minimal, explicit assumptions needed so the design is concrete.